Human-swarm Interactions for Formation Control Using Interpreters

In this work, we develop a novel Human-Swarm Interaction (HSI) framework for formation control using the notion of an interpreter, enabling the user to control a robotic swarm's shape and formation using abstraction. The user conveys their intended commands by drawing shapes through arm gestures and motions which are recorded by an off-the-shelf wearable device. We propose a novel interpreter system, which acts as an intermediary between the user and the swarm to simplify the roles of both. The interpreter takes in high level input in the form of shapes drawn by the user, and translates it into swarm control commands by planning in the shape space using novel shape morphing dynamics (SMD), which is also used for user feedback. The proposed interpreter employs machine learning, estimation and optimal control techniques to translate the users intention into swarm control parameters. The dynamics of the swarm are realized by means of a novel decentralized formation controller based on distributed linear iterations and dynamic average consensus. Theoretical guarantees of convergence along with convergence rate of the proposed swarm controller are given. The resulting shape morphing dynamics are illustrated and discussed through simulations. The entire framework is demonstrated theoretically as well as experimentally in a 2D environment, with a human controlling a swarm of simulated robots in real time with the help of a Graphical User Environemnt (GUI).